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2 results for “rural and urban locality”
Data set for "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico."
<p>Data set for original article "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico"<strong> </strong>accepted at Biotropica.</p> <p>We studied changes in coccinellid assemblage composition, diversity, species richness and abundance in domestic gardens along a rural to urban gradient in southeastern Mexico, and identified local and lanscape variables that determine their species richness and abundance. </p>
Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
<p><strong>INTRODUCTION</strong></p> <p>The goal of these LPWAN datasets is to provide the global research community with a benchmark tool to evaluate fingerprint localization algorithms in large outdoor environments with various properties. An identical collection methodology was used for all datasets: during a period of three months, numerous devices containing a GPS receiver periodically obtained new location data, which was sent to a local data server via a Sigfox or LoRaWAN message. Together with network information such as the receiving time of the message, base station IDs' of all receiving base stations and the Received Signal Strength Indicator (RSSI) per base station, this location data was stored in one of the three LPWAN datasets: </p> <ul> <li> <p><strong>lorawan_dataset_antwerp.csv</strong></p> </li> <li> <p>130 430 LoRaWAN messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_antwerp.csv</strong></p> </li> <li> <p>14 378 Sigfox messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_rural.csv</strong></p> </li> <li> <p>25 638 Sigfox messages, obtained in a rural area between Antwerp and Ghent</p> </li> </ul> <p>As the rural and urban Sigfox datasets were recorded in adjacent areas, many base stations that are located at the border of these areas can be found in both datasets. However, they do not necessarily share the same identifier: e.g. ‘BS 1’ in the urban Sigfox dataset could be the same base station as ‘BS 36’ in the rural Sigfox dataset. If the user intends to combine both Sigfox datasets, the mapping of the ID's of these base stations can be found in the file:</p> <ul> <li> <p><strong>sigfox_bs_mapping.csv</strong></p> </li> </ul> <p>The collection methodology of the datasets, and the first results of a basic fingerprinting implementation are documented in the following journal paper:<br> <a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p> </p> <p><strong>UPDATES IN VERSION 1.3</strong></p> <p>We added the file <strong>lorawan_antwerp_gateway_locations.json.txt. </strong>As the filename suggests, this file contains the coordinates of the LoRaWAN gateways in Antwerp. (The .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo).</p> <p> </p> <p><strong>UPDATES IN VERSION 1.2</strong></p> <p>In this version of the LPWAN dataset, only the LoRaWAN set has been updated. The Sigfox datasets remain identical to version 1.0 and 1.1. The main updates in the LoRaWAN set are the following:</p> <ul> <li> <p><strong>New data</strong>: the LoRaWAN messages in the new set are collected 1 year after the previous dataset version. To be consistent with the previous versions, the new LoRaWAN set is uploaded in the same .CSV format as before. This upload can still be found in this repository as ‘<strong>lorawan_dataset_antwerp.csv</strong>’.</p> </li> <li> <p><strong>More gateways</strong>: Compared to the previous dataset, 4 gateways were added to the LoRaWAN network. The RSSI of these gateways are shown in columns ‘BS 69’, ‘BS 70’,‘BS 71’ and ‘BS 72’. All other ‘BS’ columns are in the same order as in previous dataset versions.</p> </li> <li> <p><strong>More metadata: </strong>In the previous LoRaWAN dataset, metadata was limited to 3 receiving gateways per message. In the new dataset version, metadata from all receiving gateways is included in every message. Moreover, some gateways provide a timestamp with nanosecond precision, which can be used to evaluate Time Difference of Arrival localization methods with LoRaWAN.</p> </li> <li> <p><strong>2 file formats: </strong>As more metadata becomes available, we find it important to share the dataset in a clearer overview. This also allows researchers to evaluate the performance of LoRaWAN in an urban environment. Therefore, we publish the new LoRaWAN dataset as a .CSV file as described above, but also as a .JSON file (<strong>lorawan_antwerp_2019_dataset.json.txt,</strong> the .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo) An example of one message in this JSON format can be seen below:</p> <ul> <li> <p>JSON format description:</p> <ul> <li> <p><strong>HDOP</strong>: Horizontal Dilution of Precision</p> </li> <li> <p><strong>dev_addr: </strong>LoRaWAN device address</p> </li> <li> <p><strong>dev_eui: </strong>LoRaWAN device EUI</p> </li> <li> <p><strong>sf: </strong>Spreading factor</p> </li> <li> <p><strong>channel: </strong>TX channel (EU region)</p> </li> <li> <p><strong>payload: </strong>application payload</p> </li> <li> <p><strong>adr: </strong>Adaptive Data Rate (1 = enabled, 0= disabled)</p> </li> <li> <p><strong>counter: </strong>device uplink message counter</p> </li> <li> <p><strong>latitude: </strong>Groundtruth TX location latitude</p> </li> <li> <p><strong>longitude: </strong>Groundtruth TX location longitude</p> </li> <li> <p><strong>airtime: </strong>signal airtime (seconds)</p> </li> <li> <p><strong>gateways:</strong></p> <ul> <li> <p><strong>rssi: </strong>Received Signal Strength</p> </li> <li> <p><strong>esp: </strong>Estimated Signal Power</p> </li> <li> <p><strong>snr: </strong>Signal-to-Noise Ratio</p> </li> <li> <p><strong>ts_type: </strong>Timestamp type. If this says "GPS_RADIO", a nanosecond precise timestamp is available</p> </li> <li> <p><strong>time: </strong>time of arrival at the gateway</p> </li> <li> <p><strong>id: </strong>gateway ID</p> </li> </ul> </li> </ul> </li> <li> <p>JSON example</p> <pre><code class="language-json">{ "hdop": 0.7, "dev_addr": "07000EFE", "payload": "008d000392d54c4284d18c403333333f04682aa9410500e8fd4106cabdbc420f00db0d470ce32ac93f0d582be93f0bfa3f8d3f", "adr": 1, "latitude": 51.20856475830078, "counter": 31952, "longitude": 4.400575637817383, "airtime": 0.112896, "gateways": [ { "rssi": -115, "esp": -115.832695, "snr": 6.75, "rx_time": { "ts_type": "None", "time": "2019-01-04T08:59:53.079+01:00" }, "id": "08060716" }, { "rssi": -116, "esp": -125.51497, "snr": -9.0, "rx_time": { "ts_type": "GPS_RADIO", "time": "2019-01-04T08:59:53.962029179+01:00" }, "id": "FF0178DF" } ], "dev_eui": "3432333853376B18", "sf": 7, "channel": 8 }</code></pre> <p> </p> </li> </ul> </li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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